We have focussed on developing datasets for gene expression using RNA-Seq, which allows us to apply a standard set of methods to a variety of model systems. Using this approach we have contributed to a number of different studies. One of the most interesting areas is in the application to the human brain, where we have a large series of brains with information on both genetic variability and gene expression. Our data has been used in many studies to determine whether a nominated genetic variant associated with a given disease or other phenotype, has a proximal biological effect on gene expression. We have published that there are multiple strong effects of aging on gene expression in the human brain. We have shown that, while there are certainly changes in the cellular composition of the brain as humans age, there is an additional and distinct signal driven by loss of synaptic genes more specifically. We are currently extending these results to larger datasets and other species. We have also been involved in several studies looking at the effects of neurological disease on gene expression. An interesting observation in Lewy body dementia was that there are dramatic gene expression changes in areas without obvious pathology but that are implicated in symptomatology. These results show that we can apply gene expression to complex human diseases, avoiding confounds of neuronal loss.